OpenRaiser/Intern-Atlas huggingface dataset:
Achievement of Minimized Combinatorial Test Suite for Configuration-Aware Software Functional Testing Using the Cuckoo Search Algorithm
Venue: arXiv cs.SE 2019
Year: 2019

Context: Software has become an innovative solution nowadays for many applications and methods in science and engineering. Ensuring the quality and correctness of software is challenging because each program has different configurations and input domains. To ensure the quality of software, all possible configurations and input combinations need to be evaluated against their expected outputs. However, this exhaustive test is impractical because of time and resource constraints due to the large domain of input and configurations. Thus, different sampling techniques have been used to sample these input domains and configurations. Objective: Combinatorial testing can be used to effectively detect faults in software-under-test. This technique uses combinatorial optimization concepts to systematically minimize the number of test cases by considering the combinations of inputs. This paper proposes a new strategy to generate combinatorial test suite by using cuckoo search concepts. Method: Cuckoo Search is used in the design and implementation of a strategy to construct optimized combinatorial sets. The strategy consists of different algorithms for construction. These algorithms are combined to serve the Cuckoo Search. Results: The efficiency and performance of the new technique were proven through different experiment sets. The effectiveness of the strategy is assessed by applying the generated test suites on a real-world case study for the purpose of functional testing. Conclusion: Results show that the generated test suites can detect faults effectively. In addition, the strategy also opens a new direction for the application of Cuckoo Search in the context of software engineering.
Optimizing Majority Voting Based Systems Under a Resource Constraint for Multiclass Problems
Venue: arXiv cs.AI 2019
Year: 2019

Ensemble-based approaches are very effective in various fields in raising the accuracy of its individual members, when some voting rule is applied for aggregating the individual decisions. In this paper, we investigate how to find and characterize the ensembles having the highest accuracy if the total cost of the ensemble members is bounded. This question leads to Knapsack problem with non-linear and non-separable objective function in binary and multiclass classification if the majority voting is chosen for the aggregation. As the conventional solving methods cannot be applied for this task, a novel stochastic approach was introduced in the binary case where the energy function is discussed as the joint probability function of the member accuracy. We show some theoretical results with respect to the expected ensemble accuracy and its variance in the multiclass classification problem which can help us to solve the Knapsack problem.
3D mapping for multi hybrid robot cooperation
Venue: arXiv cs.RO 2019
Year: 2019

This paper presents a novel approach to build consistent 3D maps for multi robot cooperation in USAR environments. The sensor streams from unmanned aerial vehicles (UAVs) and ground robots (UGV) are fused in one consistent map. The UAV camera data are used to generate 3D point clouds that are fused with the 3D point clouds generated by a rolling 2D laser scanner at the UGV. The registration method is based on the matching of corresponding planar segments that are extracted from the point clouds. Based on the registration, an approach for a globally optimized localization is presented. Apart from the structural information of the point clouds, it is important to mention that no further information is required for the localization. Two examples show the performance of the overall registration.
A Robust Visual System for Small Target Motion Detection Against Cluttered Moving Backgrounds
Venue: arXiv cs.CV 2019
Year: 2019

Monitoring small objects against cluttered moving backgrounds is a huge challenge to future robotic vision systems. As a source of inspiration, insects are quite apt at searching for mates and tracking prey -- which always appear as small dim speckles in the visual field. The exquisite sensitivity of insects for small target motion, as revealed recently, is coming from a class of specific neurons called small target motion detectors (STMDs). Although a few STMD-based models have been proposed, these existing models only use motion information for small target detection and cannot discriminate small targets from small-target-like background features (named as fake features). To address this problem, this paper proposes a novel visual system model (STMD+) for small target motion detection, which is composed of four subsystems -- ommatidia, motion pathway, contrast pathway and mushroom body. Compared to existing STMD-based models, the additional contrast pathway extracts directional contrast from luminance signals to eliminate false positive background motion. The directional contrast and the extracted motion information by the motion pathway are integrated in the mushroom body for small target discrimination. Extensive experiments showed the significant and consistent improvements of the proposed visual system model over existing STMD-based models against fake features.
Lecturer Performance System Using Neural Network with Particle Swarm Optimization
Venue: arXiv cs.NE 2019
Year: 2019

The field of analyzing performance is very important and sensitive in particular when it is related to the performance of lecturers in academic institutions. Locating the weak points of lecturers through a system that provides an early warning to notify or reward the lecturers with warned or punished notices will help them to improve their weaknesses, leads to a better quality in the institutions. The current system has major issues in the higher education at Salahaddin University-Erbil (SUE) in Kurdistan-Iraq. These issues are: first, the assessment of lecturers' activities is conducted traditionally via the Quality Assurance Teams at different departments and colleges at the university, second, the outcomes in some cases of lecturers' performance provoke a low level of acceptance among lectures, as these cases are reflected and viewed by some academic communities as unfair cases, and finally, the current system is not accurate and vigorous. In this paper, Particle Swarm Optimization Neural Network is used to assess performance of lecturers in more fruitful way and also to enhance the accuracy of recognition system. Different real and novel data sets are collected from SUE. The prepared datasets preprocessed and important features are then fed as input source to the training and testing phases. Particle Swarm Optimization is used to find the best weights and biases in the training phase of the neural network. The best accuracy rate obtained in the test phase is 98.28 %.
Passive TCP Identification for Wired and WirelessNetworks: A Long-Short Term Memory Approach
Venue: arXiv cs.NI 2019
Year: 2019

Transmission control protocol (TCP) congestion control is one of the key techniques to improve network performance. TCP congestion control algorithm identification (TCP identification) can be used to significantly improve network efficiency. Existing TCP identification methods can only be applied to limited number of TCP congestion control algorithms and focus on wired networks. In this paper, we proposed a machine learning based passive TCP identification method for wired and wireless networks. After comparing among three typical machine learning models, we concluded that the 4-layers Long Short Term Memory (LSTM) model achieves the best identification accuracy. Our approach achieves better than 98% accuracy in wired and wireless networks and works for newly proposed TCP congestion control algorithms.
Uncertainty Measures and Prediction Quality Rating for the Semantic Segmentation of Nested Multi Resolution Street Scene Images
Venue: arXiv cs.CV 2019
Year: 2019

In the semantic segmentation of street scenes the reliability of the prediction and therefore uncertainty measures are of highest interest. We present a method that generates for each input image a hierarchy of nested crops around the image center and presents these, all re-scaled to the same size, to a neural network for semantic segmentation. The resulting softmax outputs are then post processed such that we can investigate mean and variance over all image crops as well as mean and variance of uncertainty heat maps obtained from pixel-wise uncertainty measures, like the entropy, applied to each crop's softmax output. In our tests, we use the publicly available DeepLabv3+ MobilenetV2 network (trained on the Cityscapes dataset) and demonstrate that the incorporation of crops improves the quality of the prediction and that we obtain more reliable uncertainty measures. These are then aggregated over predicted segments for either classifying between IoU=0 and IoU>0 (meta classification) or predicting the IoU via linear regression (meta regression). The latter yields reliable performance estimates for segmentation networks, in particular useful in the absence of ground truth. For the task of meta classification we obtain a classification accuracy of $81.93\%$ and an AUROC of $89.89\%$. For meta regression we obtain an $R^2$ value of $84.77\%$. These results yield significant improvements compared to other approaches.
Toward Spectral and Energy Efficient 5G Networks Using Relayed OFDM with Index Modulation
Venue: arXiv cs.IT 2019
Year: 2019

Next generation wireless networks are expected to provide much higher data throughput and reliable connections for a far larger number of wireless service subscribers and machine-type nodes, which result in increasingly stringent requirements of spectral efficiency (SE) and energy efficiency (EE). Orthogonal frequency-division multiplexing with index modulation (OFDM-IM) stands out as a promising solution to satisfy the SE requirement with a reasonable increase in system complexity. However, the EE of OFDM-IM is still required to be enhanced. Moreover, diversity gain is hardly harvested in OFDM-IM systems, which hinders further reliability enhancement. In this regard, relay assisted OFDM-IM, as a promising joint paradigm to achieve both high SE and EE, was proposed and has been studied since last year. The objectives of this article are to summarize the recent achievements of this joint paradigm, articulate its pros and cons, and reveal the corresponding challenges and future work.
Crossmodal Voice Conversion
Venue: arXiv cs.SD 2019
Year: 2019

Humans are able to imagine a person's voice from the person's appearance and imagine the person's appearance from his/her voice. In this paper, we make the first attempt to develop a method that can convert speech into a voice that matches an input face image and generate a face image that matches the voice of the input speech by leveraging the correlation between faces and voices. We propose a model, consisting of a speech converter, a face encoder/decoder and a voice encoder. We use the latent code of an input face image encoded by the face encoder as the auxiliary input into the speech converter and train the speech converter so that the original latent code can be recovered from the generated speech by the voice encoder. We also train the face decoder along with the face encoder to ensure that the latent code will contain sufficient information to reconstruct the input face image. We confirmed experimentally that a speech converter trained in this way was able to convert input speech into a voice that matched an input face image and that the voice encoder and face decoder can be used to generate a face image that matches the voice of the input speech.
Enabling the Social Internet of Things and Social Cloud
Venue: arXiv cs.SI 2019
Year: 2019

Social Internet of Things are changing what social patterns can be, and will bring unprecedented online and offline social experiences. Social cloud is an improvement over social network in order to cooperatively provide computing facilities through social interactions. Both of these two field needs more research efforts to have a generic or unified supporting architecture, in order to integrate with various involved technologies. These two paradigms are both related to Social Networks, Cloud Computing, and Internet of Things. Therefore, we have reasons to believe that they have many potentials to support each other, and we predict that the two will be merged in one way or another.
Simultaneous Contact, Gait and Motion Planning for Robust Multi-Legged Locomotion via Mixed-Integer Convex Optimization
Venue: arXiv cs.RO 2019
Year: 2019

Traditional motion planning approaches for multi-legged locomotion divide the problem into several stages, such as contact search and trajectory generation. However, reasoning about contacts and motions simultaneously is crucial for the generation of complex whole-body behaviors. Currently, coupling theses problems has required either the assumption of a fixed gait sequence and flat terrain condition, or non-convex optimization with intractable computation time. In this paper, we propose a mixed-integer convex formulation to plan simultaneously contact locations, gait transitions and motion, in a computationally efficient fashion. In contrast to previous works, our approach is not limited to flat terrain nor to a pre-specified gait sequence. Instead, we incorporate the friction cone stability margin, approximate the robot's torque limits, and plan the gait using mixed-integer convex constraints. We experimentally validated our approach on the HyQ robot by traversing different challenging terrains, where non-convexity and flat terrain assumptions might lead to sub-optimal or unstable plans. Our method increases the motion generality while keeping a low computation time.
The Last Mile: High-Assurance and High-Speed Cryptographic Implementations
Venue: arXiv cs.CR 2019
Year: 2019

We develop a new approach for building cryptographic implementations. Our approach goes the last mile and delivers assembly code that is provably functionally correct, protected against side-channels, and as efficient as hand-written assembly. We illustrate ur approach using ChaCha20-Poly1305, one of the mandatory ciphersuites in TLS 1.3, and deliver formally verified vectorized implementations which outperform the fastest non-verified code. We realize our approach by combining the Jasmin framework, which offers in a single language features of high-level and low-level programming, and the EasyCrypt proof assistant, which offers a versatile verification infrastructure that supports proofs of functional correctness and equivalence checking. Neither of these tools had been used for functional correctness before. Taken together, these infrastructures empower programmers to develop efficient and verified implementations by "game hopping", starting from reference implementations that are proved functionally correct against a specification, and gradually introducing program optimizations that are proved correct by equivalence checking. We also make several contributions of independent interest, including a new and extensible verified compiler for Jasmin, with a richer memory model and support for vectorized instructions, and a new embedding of Jasmin in EasyCrypt.
Fast Enhanced CT Metal Artifact Reduction using Data Domain Deep Learning
Venue: arXiv cs.CV 2019
Year: 2019

Filtered back projection (FBP) is the most widely used method for image reconstruction in X-ray computed tomography (CT) scanners. The presence of hyper-dense materials in a scene, such as metals, can strongly attenuate X-rays, producing severe streaking artifacts in the reconstruction. These metal artifacts can greatly limit subsequent object delineation and information extraction from the images, restricting their diagnostic value. This problem is particularly acute in the security domain, where there is great heterogeneity in the objects that can appear in a scene, highly accurate decisions must be made quickly. The standard practical approaches to reducing metal artifacts in CT imagery are either simplistic non-adaptive interpolation-based projection data completion methods or direct image post-processing methods. These standard approaches have had limited success. Motivated primarily by security applications, we present a new deep-learning-based metal artifact reduction (MAR) approach that tackles the problem in the projection data domain. We treat the projection data corresponding to metal objects as missing data and train an adversarial deep network to complete the missing data in the projection domain. The subsequent complete projection data is then used with FBP to reconstruct image intended to be free of artifacts. This new approach results in an end-to-end MAR algorithm that is computationally efficient so practical and fits well into existing CT workflows allowing easy adoption in existing scanners. Training deep networks can be challenging, and another contribution of our work is to demonstrate that training data generated using an accurate X-ray simulation can be used to successfully train the deep network when combined with transfer learning using limited real data sets. We demonstrate the effectiveness and potential of our algorithm on simulated and real examples.
A Graph-based Model for Joint Chinese Word Segmentation and Dependency Parsing
Venue: arXiv cs.CL 2019
Year: 2019

Chinese word segmentation and dependency parsing are two fundamental tasks for Chinese natural language processing. The dependency parsing is defined on word-level. Therefore word segmentation is the precondition of dependency parsing, which makes dependency parsing suffer from error propagation and unable to directly make use of the character-level pre-trained language model (such as BERT). In this paper, we propose a graph-based model to integrate Chinese word segmentation and dependency parsing. Different from previous transition-based joint models, our proposed model is more concise, which results in fewer efforts of feature engineering. Our graph-based joint model achieves better performance than previous joint models and state-of-the-art results in both Chinese word segmentation and dependency parsing. Besides, when BERT is combined, our model can substantially reduce the performance gap of dependency parsing between joint models and gold-segmented word-based models. Our code is publicly available at https://github.com/fastnlp/JointCwsParser.
Modeling Corruption in Eventually-Consistent Graph Databases
Venue: arXiv cs.DB 2019
Year: 2019

We present a model and analysis of an eventually consistent graph database where loosely cooperating servers accept concurrent updates to a partitioned, distributed graph. The model is high-fidelity and preserves design choices from contemporary graph database management systems. To explore the problem space, we use two common graph topologies as data models for realistic experimentation. The analysis reveals, even assuming completely fault-free hardware and bug-free software, that if it is possible for updates to interfere with one-another, corruption will occur and spread significantly through the graph within the production database lifetime. Using our model, database designers and operators can compute the rate of corruption for their systems and determine whether they are sufficiently dependable for their intended use.
Citation gaming induced by bibliometric evaluation: a country-level comparative analysis
Venue: arXiv cs.DL 2019
Year: 2019

It is several years since national research evaluation systems around the globe started making use of quantitative indicators to measure the performance of researchers. Nevertheless, the effects on these systems on the behavior of the evaluated researchers are still largely unknown. We attempt to shed light on this topic by investigating how Italian researchers reacted to the introduction in 2011 of national regulations in which key passages of professional careers are governed by bibliometric indicators. A new inwardness measure, able to gauge the degree of scientific self-referentiality of a country, is defined as the proportion of citations coming from the country itself compared to the total number of citations gathered by the country. Compared to the trends of the other G10 countries in the period 2000-2016, Italy's inwardness shows a net increase after the introduction of the new evaluation rules. Indeed, globally and also for a large majority of the research fields, Italy became the European country with the highest inwardness. Possible explanations are proposed and discussed, concluding that the observed trends are strongly suggestive of a generalized strategic use of citations, both in the form of author self-citations and of citation clubs. We argue that the Italian case offers crucial insights on the constitutive effects of evaluation systems. As such, it could become a paradigmatic case in the debate about the use of indicators in science-policy contexts.
Polynomial Pass Lower Bounds for Graph Streaming Algorithms
Venue: arXiv cs.DS 2019
Year: 2019

We present new lower bounds that show that a polynomial number of passes are necessary for solving some fundamental graph problems in the streaming model of computation. For instance, we show that any streaming algorithm that finds a weighted minimum $s$-$t$ cut in an $n$-vertex undirected graph requires $n^{2-o(1)}$ space unless it makes $n^{Ω(1)}$ passes over the stream. To prove our lower bounds, we introduce and analyze a new four-player communication problem that we refer to as the hidden-pointer chasing problem. This is a problem in spirit of the standard pointer chasing problem with the key difference that the pointers in this problem are hidden to players and finding each one of them requires solving another communication problem, namely the set intersection problem. Our lower bounds for graph problems are then obtained by reductions from the hidden-pointer chasing problem. Our hidden-pointer chasing problem appears flexible enough to find other applications and is therefore interesting in its own right. To showcase this, we further present an interesting application of this problem beyond streaming algorithms. Using a reduction from hidden-pointer chasing, we prove that any algorithm for submodular function minimization needs to make $n^{2-o(1)}$ value queries to the function unless it has a polynomial degree of adaptivity.
Interval Prediction for Continuous-Time Systems with Parametric Uncertainties
Venue: arXiv cs.SY 2019
Year: 2019

The problem of behaviour prediction for linear parameter-varying systems is considered in the interval framework. It is assumed that the system is subject to uncertain inputs and the vector of scheduling parameters is unmeasurable, but all uncertainties take values in a given admissible set. Then an interval predictor is designed and its stability is guaranteed applying Lyapunov function with a novel structure. The conditions of stability are formulated in the form of linear matrix inequalities. Efficiency of the theoretical results is demonstrated in the application to safe motion planning for autonomous vehicles.